Abstract
In this chapter we propose a method to select from a possibly large set of observable quantities a minimal subset yielding (nearly) all relevant information on the quantity we are going to predict. We derive the theoretical background and give numerical hints and examples, including results for some daily dollar exchange rates. Our approach essentially profits from the availability of a fast algorithm for mutual information analysis.
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Pompe, B. (2002). Mutual Information and Relevant Variables for Predictions. In: Soofi, A.S., Cao, L. (eds) Modelling and Forecasting Financial Data. Studies in Computational Finance, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0931-8_4
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DOI: https://doi.org/10.1007/978-1-4615-0931-8_4
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